At present, it is difficult to obtain optimal MR imaging on pediatric patients, especially during the first year of life. One of the main reasons for this difficulty is the process of acquisition parameter selection to produce images of the highest possible diagnostic contrast is highly dependent upon imaging practitioners who may be unaware of appropriate parameter selection. This issue and the underlying processes at work have been well documented in the medical scientific community (see, for example, an article by M. S. van der Knaap and J. Valk, entitled “MR imaging of the various stages of normal myelination during the first year of life”, Neuroradiology, 1990, pp 459-470, vol. 31(6), Springer-Verlag).
Thus, age specific optimization of acquisition parameters depends solely on the knowledge of the imaging practitioner. Further, there are no known current systems to provide this type of guidance. At this time, one must rely on the subjective assumptions and experience (which is varying) of imaging practitioners to set appropriate parameters at the time of acquisition. Accordingly, it is highly likely that optimal image contrast is often simply not achieved.
Automation and documentation of this process would be beneficial. The MR imaging acquisition parameters, T2 and T1 relaxation times, would be of particular interest, but it is fair to say that even diffusion based contrast mechanisms could be automated. This is based on work done in kitten models as reported in an article by Corrado Baratti, Alan S. Barnett, and Carlo Pierpaoli, entitled “Comparative MR Imaging Study of Brain Maturation in Kittens with T1, T2, and the Trace of the Diffusion Tensor”, Radiology, January 1999, pp. 133-142, vol. 210, which is incorporated by reference herein. Regardless, the largest advantage of an automated, documented approach is that it removes the practitioner's subjectiveness and/or potential lack of experience, and replaces it with a process that utilizes scientifically documented parameter choices.
It is noted that methods that generally relate to automated parameter selection are known. One example is U.S. Patent Publication 2006/0153436 by Gabriel Haras, dated Jul. 13, 2006 and entitled “Method for Determining Acquisition Parameters for a Medical Tomography Device, and an Associated Apparatus.” In general, the goal of the described apparatus is to automate certain parameter selection in “Tomographic” imagers, specifically, computed tomographic imagers. Protocol limitations are outlined as well as changes in body habitus as a guide for setting optimal parameters. The described apparatus lends itself to the automation of parameter sets, but more so for x-ray based imaging methods. Additional protocol alterations relate directly to x-ray quantity and quality factors. Imaging of children is discussed, but the scope of parameter adjustment is not broadened to easily incorporate MR-related contrast mechanisms so as to address the above-discussed problem.
Another example is U.S. Patent Publication 2010/0092056 by Neil M. Rofsky and Daniel K. Sodickson, dated Apr. 15, 2010 and entitled “MRI Systems and Related Methods.” Many parameters are selected for automation, but the basis for alteration is not described in detail. Furthermore, it appears that the described system relates to future developments in three dimensional imaging and fast scanning techniques. No method to determine appropriate parameter selection for image contrast is discussed therein.